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---
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- svenwey/LogDataset
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: logdataset1024samples15epochs_llama3-1_16bit_64eval_natural_adalora_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# logdataset1024samples15epochs_llama3-1_16bit_64eval_natural_adalora_2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the svenwey/LogDataset dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0752
- Model Preparation Time: 0.0137
- Linecount Difference Smape Score: 0.7773
- Linecontentlength Difference Smape Score: 0.6338
- Linecontent Sacrebleu Score: 0.4516
- Linecontent Sacrebleu Withoutexplicitnumbers Score: 0.4777
- Timestamps Smape Difference Score: 0.7332
- Timestamps Formatconsistency Score: 1.0
- Timestamps Monotinicity Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Linecount Difference Smape Score | Linecontentlength Difference Smape Score | Linecontent Sacrebleu Score | Linecontent Sacrebleu Withoutexplicitnumbers Score | Timestamps Smape Difference Score | Timestamps Formatconsistency Score | Timestamps Monotinicity Score |
|:-------------:|:-------:|:-----:|:---------------:|:----------------------:|:--------------------------------:|:----------------------------------------:|:---------------------------:|:--------------------------------------------------:|:---------------------------------:|:----------------------------------:|:-----------------------------:|
| 1.0869 | 0.9998 | 1147 | 1.1504 | 0.0137 | 0.7016 | 0.5256 | 0.3531 | 0.3637 | 0.6414 | 0.9531 | 0.9531 |
| 0.6665 | 1.9996 | 2294 | 1.5606 | 0.0137 | 0.7120 | 0.5669 | 0.4394 | 0.4462 | 0.6706 | 0.9844 | 0.9844 |
| 0.7854 | 2.9993 | 3441 | 1.7849 | 0.0137 | 0.7278 | 0.5911 | 0.4532 | 0.4643 | 0.6926 | 0.9688 | 0.9688 |
| 0.9092 | 4.0 | 4589 | 1.9795 | 0.0137 | 0.7370 | 0.5918 | 0.4415 | 0.4605 | 0.6926 | 0.9688 | 0.9688 |
| 1.0273 | 4.9998 | 5736 | 2.1496 | 0.0137 | 0.7426 | 0.5876 | 0.4323 | 0.4541 | 0.6975 | 0.9844 | 0.9844 |
| 1.1379 | 5.9996 | 6883 | 2.3043 | 0.0137 | 0.7554 | 0.6070 | 0.4534 | 0.4805 | 0.7112 | 0.9844 | 0.9844 |
| 1.2381 | 6.9993 | 8030 | 2.4486 | 0.0137 | 0.7618 | 0.6078 | 0.4553 | 0.4755 | 0.7194 | 0.9844 | 0.9844 |
| 1.326 | 8.0 | 9178 | 2.5735 | 0.0137 | 0.7655 | 0.6224 | 0.4573 | 0.4797 | 0.7235 | 1.0 | 1.0 |
| 1.4048 | 8.9998 | 10325 | 2.7091 | 0.0137 | 0.7602 | 0.6199 | 0.4522 | 0.4765 | 0.7160 | 1.0 | 1.0 |
| 1.4702 | 9.9996 | 11472 | 2.8050 | 0.0137 | 0.7396 | 0.5926 | 0.4319 | 0.4448 | 0.6944 | 0.9844 | 0.9844 |
| 1.5236 | 10.9993 | 12619 | 2.9013 | 0.0137 | 0.7473 | 0.6007 | 0.4234 | 0.4437 | 0.6984 | 1.0 | 1.0 |
| 1.5639 | 12.0 | 13767 | 2.9654 | 0.0137 | 0.7528 | 0.6115 | 0.4416 | 0.4739 | 0.7073 | 1.0 | 1.0 |
| 1.5956 | 12.9998 | 14914 | 3.0193 | 0.0137 | 0.7666 | 0.6197 | 0.4456 | 0.4609 | 0.7295 | 1.0 | 1.0 |
| 1.6152 | 13.9996 | 16061 | 3.0513 | 0.0137 | 0.7411 | 0.5917 | 0.4208 | 0.4372 | 0.6896 | 0.9844 | 0.9844 |
| 1.6235 | 14.9967 | 17205 | 3.0752 | 0.0137 | 0.7773 | 0.6338 | 0.4516 | 0.4777 | 0.7332 | 1.0 | 1.0 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |